from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting("sklearnex", config="config.yml")
reporting.make_report()

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.010	0.0	NaN	7.720	0.0	-1	1	NaN	0.048	0.005	0.215	0.217	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	NaN	7.213	0.0	-1	5	NaN	0.046	0.001	0.240	0.240	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	NaN	7.484	0.0	1	100	NaN	0.045	0.001	0.237	0.237	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	NaN	7.137	0.0	-1	100	NaN	0.046	0.001	0.245	0.245	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	NaN	7.613	0.0	1	5	NaN	0.045	0.001	0.232	0.232	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	NaN	7.471	0.0	1	1	NaN	0.046	0.001	0.233	0.233	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	NaN	0.364	0.0	-1	1	NaN	0.009	0.001	0.514	0.515	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	NaN	0.360	0.0	-1	5	NaN	0.009	0.001	0.518	0.519	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	NaN	0.377	0.0	1	100	NaN	0.009	0.000	0.472	0.472	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	NaN	0.372	0.0	-1	100	NaN	0.009	0.000	0.476	0.476	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	NaN	0.350	0.0	1	5	NaN	0.009	0.000	0.498	0.499	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	NaN	0.388	0.0	1	1	NaN	0.008	0.000	0.517	0.517	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.865	0.128	NaN	0.000	0.002	-1	1	0.663	0.194	0.016	9.629	9.661	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.003	NaN	0.000	0.021	-1	1	1.000	0.008	0.000	2.678	2.678	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.624	0.043	NaN	0.000	0.003	-1	5	0.757	0.191	0.003	13.732	13.734	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.001	NaN	0.000	0.023	-1	5	1.000	0.008	0.000	2.949	2.950	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.016	0.060	NaN	0.000	0.002	1	100	0.882	0.224	0.005	8.999	9.002	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.020	0.001	NaN	0.000	0.020	1	100	1.000	0.008	0.000	2.513	2.515	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.633	0.060	NaN	0.000	0.003	-1	100	0.882	0.223	0.005	11.814	11.816	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.004	NaN	0.000	0.025	-1	100	1.000	0.008	0.001	3.092	3.114	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.988	0.035	NaN	0.000	0.002	1	5	0.757	0.184	0.005	10.804	10.809	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.001	NaN	0.000	0.019	1	5	1.000	0.008	0.000	2.452	2.453	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.091	0.010	NaN	0.001	0.001	1	1	0.663	0.182	0.005	6.000	6.003	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.020	0.003	NaN	0.000	0.020	1	1	1.000	0.007	0.000	2.780	2.781	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.642	0.019	NaN	0.000	0.002	-1	1	0.896	0.027	0.001	60.056	60.105	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.004	0.002	NaN	0.000	0.004	-1	1	1.000	0.001	0.000	6.258	6.299	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.482	0.020	NaN	0.000	0.002	-1	5	0.922	0.029	0.002	86.413	86.554	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.005	0.002	NaN	0.000	0.005	-1	5	1.000	0.001	0.000	7.722	7.757	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.905	0.054	NaN	0.000	0.002	1	100	0.929	0.064	0.004	29.535	29.593	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	NaN	0.000	0.003	1	100	1.000	0.001	0.000	3.784	3.818	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.556	0.034	NaN	0.000	0.003	-1	100	0.929	0.066	0.002	38.570	38.580	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.003	NaN	0.000	0.006	-1	100	1.000	0.001	0.000	7.436	7.459	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.899	0.022	NaN	0.000	0.002	1	5	0.922	0.027	0.001	69.820	69.845	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	NaN	0.000	0.003	1	5	1.000	0.001	0.000	4.520	4.562	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.018	0.007	NaN	0.000	0.001	1	1	0.896	0.026	0.002	38.449	38.523	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	NaN	0.000	0.002	1	1	1.000	0.001	0.000	2.951	3.000	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2.625	0.025	NaN	0.030	0.0	-1	1	NaN	0.861	0.346	3.050	3.287	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.307	0.065	NaN	0.024	0.0	-1	5	NaN	0.699	0.040	4.729	4.736	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.250	0.049	NaN	0.025	0.0	1	100	NaN	0.737	0.015	4.413	4.414	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.236	0.076	NaN	0.025	0.0	-1	100	NaN	0.667	0.010	4.851	4.851	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.251	0.062	NaN	0.025	0.0	1	5	NaN	0.733	0.016	4.432	4.433	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.241	0.049	NaN	0.025	0.0	1	1	NaN	0.678	0.014	4.784	4.785	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	NaN	0.017	0.0	-1	1	NaN	0.004	0.002	0.239	0.280	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.031	0.0	-1	5	NaN	0.002	0.002	0.242	0.310	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.000	0.000	NaN	0.035	0.0	1	100	NaN	0.001	0.001	0.445	0.604	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.029	0.0	-1	100	NaN	0.001	0.000	0.552	0.558	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.000	0.000	NaN	0.032	0.0	1	5	NaN	0.001	0.000	0.566	0.567	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.000	0.000	NaN	0.035	0.0	1	1	NaN	0.001	0.000	0.579	0.582	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.804	0.997	NaN	0.000	0.001	-1	1	0.929	0.102	0.004	7.893	7.899	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.000	NaN	0.000	0.003	-1	1	1.000	0.000	0.000	10.033	10.382	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.938	0.337	NaN	0.000	0.001	-1	5	0.946	0.175	0.003	5.350	5.351	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	-1	5	1.000	0.000	0.000	7.537	7.782	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	4.839	0.414	NaN	0.000	0.005	1	100	0.951	0.536	0.011	9.020	9.022	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	1	100	1.000	0.001	0.000	4.063	4.186	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2.917	0.281	NaN	0.000	0.003	-1	100	0.951	0.526	0.016	5.546	5.549	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	NaN	0.000	0.005	-1	100	1.000	0.001	0.000	6.651	6.872	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.555	0.363	NaN	0.000	0.002	1	5	0.946	0.183	0.004	8.484	8.486	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	NaN	0.000	0.002	1	5	1.000	0.000	0.000	3.854	3.975	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.840	0.304	NaN	0.000	0.001	1	1	0.929	0.096	0.002	8.762	8.763	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	NaN	0.000	0.001	1	1	1.000	0.000	0.000	4.047	4.252	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.026	0.020	NaN	0.001	0.000	-1	1	0.891	0.000	0.000	72.439	74.303	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.001	NaN	0.000	0.002	-1	1	1.000	0.000	0.000	30.269	31.194	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.021	0.002	NaN	0.001	0.000	-1	5	0.911	0.001	0.000	33.631	34.207	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	NaN	0.000	0.002	-1	5	1.000	0.000	0.000	27.814	28.676	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.034	0.011	NaN	0.000	0.000	1	100	0.894	0.004	0.000	7.692	7.730	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	100	1.000	0.000	0.000	6.567	6.831	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.038	0.005	NaN	0.000	0.000	-1	100	0.894	0.005	0.001	7.512	7.742	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.001	NaN	0.000	0.002	-1	100	1.000	0.000	0.000	23.804	24.293	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.020	0.002	NaN	0.001	0.000	1	5	0.911	0.001	0.000	30.763	30.869	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.000	0.000	NaN	0.000	0.000	1	5	1.000	0.000	0.000	5.066	5.353	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.017	0.001	NaN	0.001	0.000	1	1	0.891	0.000	0.000	42.908	43.165	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	1	1.000	0.000	0.000	8.085	8.329	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.500	0.066	30	0.032	0.0	random	NaN	0.405	0.033	1.235	1.239	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.558	0.014	30	0.029	0.0	k-means++	NaN	0.421	0.034	1.325	1.329	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.312	0.259	30	0.151	0.0	random	NaN	2.590	0.070	2.051	2.052	See	See
9	KMeans_tall	fit	1000000	1000000	100	5.503	0.028	30	0.145	0.0	k-means++	NaN	2.669	0.049	2.062	2.062	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.000	30	0.009	0.000	random	0.001	0.0	0.0	9.120	12.723	See	See
2	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	random	1.000	0.0	0.0	8.726	13.163	See	See
4	KMeans_tall	predict	1000000	1000	2	0.001	0.000	30	0.011	0.000	k-means++	0.001	0.0	0.0	10.285	11.411	See	See
5	KMeans_tall	predict	1000000	1	2	0.002	0.001	30	0.000	0.002	k-means++	1.000	0.0	0.0	17.615	18.591	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.412	0.000	random	0.002	0.0	0.0	7.422	7.926	See	See
8	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	random	1.000	0.0	0.0	13.750	14.196	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.437	0.000	k-means++	0.002	0.0	0.0	6.811	7.174	See	See
11	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.001	0.002	k-means++	1.000	0.0	0.0	14.222	14.879	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.074	0.003	20	0.002	0.0	random	NaN	0.026	0.003	2.862	2.877	See	See
3	KMeans_short	fit	10000	10000	2	0.211	0.006	20	0.001	0.0	k-means++	NaN	0.081	0.003	2.602	2.604	See	See
6	KMeans_short	fit	10000	10000	100	0.201	0.006	20	0.040	0.0	random	NaN	0.113	0.013	1.778	1.789	See	See
9	KMeans_short	fit	10000	10000	100	0.576	0.011	20	0.014	0.0	k-means++	NaN	0.310	0.007	1.858	1.858	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	random	0.000	0.000	0.0	4.843	4.865	See	See
2	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	1.000	0.000	0.0	11.740	11.994	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	k-means++	0.001	0.000	0.0	3.727	3.746	See	See
5	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	k-means++	1.000	0.000	0.0	11.093	11.423	See	See
7	KMeans_short	predict	10000	1000	100	0.002	0.0	20	0.360	0.000	random	0.279	0.001	0.0	2.015	2.018	See	See
8	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.001	0.002	random	1.000	0.000	0.0	10.626	11.009	See	See
10	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.301	0.000	k-means++	0.317	0.001	0.0	2.911	2.950	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.001	0.002	k-means++	1.000	0.000	0.0	11.446	11.532	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	10.396	0.457	[20]	0.077	0.000	NaN	NaN	NaN	NaN	NaN	1.738	0.021	5.981	5.981	See	See
3	LogisticRegression	fit	1000	1000	10000	0.857	0.573	[26]	0.093	0.001	NaN	NaN	NaN	NaN	NaN	0.719	0.030	1.191	1.192	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	1.795	0.0	NaN	NaN	NaN	NaN	0.56	0.001	0.001	0.595	1.285	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.014	0.0	NaN	NaN	NaN	NaN	1.00	0.000	0.000	0.334	0.340	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	5.012	0.0	NaN	NaN	NaN	NaN	0.35	0.005	0.001	0.348	0.365	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	1.202	0.0	NaN	NaN	NaN	NaN	0.00	0.001	0.000	0.109	0.109	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.169	0.004	NaN	0.473	0.0	NaN	NaN	NaN	0.176	0.004	0.959	0.959	See	See
3	Ridge	fit	1000000	1000000	100	1.098	0.082	NaN	0.728	0.0	NaN	NaN	NaN	0.301	0.251	3.644	4.742	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.01	0.0	NaN	8.338	0.0	NaN	NaN	0.083	0.017	0.003	0.570	0.579	See	See
2	Ridge	predict	1000	1	10000	0.00	0.0	NaN	1.526	0.0	NaN	NaN	NaN	0.000	0.000	0.487	0.522	See	See
4	Ridge	predict	1000000	1000	100	0.00	0.0	NaN	6.506	0.0	NaN	NaN	1.000	0.000	0.000	0.384	0.565	See	See
5	Ridge	predict	1000000	1	100	0.00	0.0	NaN	0.017	0.0	NaN	NaN	NaN	0.000	0.000	0.582	0.611	See	See